Cooperative execution of a genetic algorithm with an efficient training algorithm for data-driven model creation

ABSTRACT

A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to and is a continuation of U.S.patent application Ser. No. 15/489,564 entitled “COOPERATIVE EXECUTIONOF A GENETIC ALGORITHM WITH AN EFFICIENT TRAINING ALGORITHM FORDATA-DRIVEN MODEL CREATION,” filed Apr. 17, 2017, the contents of whichare incorporated herein by reference in their entirety.

BACKGROUND

Computers are often used to solve complex quantitative and qualitativeproblems. For problems that involve a large data set, a speciallytrained professional, known as a data scientist, is often hired. Thedata scientist interprets the data set and constructs models that can beprocessed by computers to solve the problem. However, hiring datascientists is cost prohibitive for many organizations.

For certain types of problems, advanced computing techniques, such asgenetic algorithms or backpropagation, may be available to develop amodel, such as a neural network, that is comparable in accuracy to amodel that would be created by a data scientist. However, geneticalgorithms may take a large number of iterations to converge on anacceptable neural network, and backpropagation may be slow when a largedata set is being modeled or when the neural network includes a largenumber of nodes, connections, or layers.

Furthermore, various types of machine-learning problems exist. Forexample, regression problems involve evaluating a series of inputs topredict a numeric output, classification problems involve evaluating aseries of inputs to predict a categorical output, and reinforcementlearning involves performing actions within an environment to optimizesome notion of a positive reward. Due to the differences in the varioustypes of problems, the available mechanisms to generate and train aneural network or other machine learning solution may beproblem-specific. For example, a support vector machine (SVM) may besuitable for some classification problems, logistic regression may besuitable for some regression problems, and a specialized machinelearning package, such as TensorFlow, may be suitable for reinforcementlearning. Thus, generating and training neural networks that meetperformance requirements for each of multiple types of problems faced byan enterprise may be slow and difficult.

SUMMARY

The present application describes automated model building systems andmethods that utilize a genetic algorithm and selective backpropagationto generate and train a neural network in a manner that is applicable tomultiple types of machine-learning problems. To illustrate, thedescribed automated model building techniques may enable a generalizedapproach to generating neural networks that can be applied forregression, classification, and reinforcement learning problems.Combining a genetic algorithm with selective backpropagation asdescribed herein may enable generating a neural network that models aparticular data set with acceptable accuracy and in less time than usinggenetic algorithms or backpropagation alone.

As an illustrative, non-limiting example, consider a home with fourtemperature sensors that periodically collect temperature readings inthe living room (L), the dining room (D), the master bedroom (M), andthe guest bedroom (G), respectively. In this example, a data set mayinclude four columns, where each column corresponds to temperaturereadings from a particular sensor in a particular room, and where eachrow corresponds to a particular time at which the four sensors took atemperature reading. It may be of interest to predict the temperature inone of the rooms, e.g., L, given the temperature in the other threerooms, e.g., D, M, and G. A neural network may be suitable for such aproblem, where the neural network has D, M, and/or G as input nodes andL as an output node. For example, a neural network that predicts anoutput value of L with 90% accuracy given input values of D, M, and/or Gmay be an acceptable solution.

In accordance with the described techniques, a combination of a geneticalgorithm and an optimization algorithm such as backpropagation, aderivative free optimizer (DFO), an extreme learning machine (ELM) orsimilar optimizer may be used to generate and then train a neuralnetwork. It is to be understood that characterization of any systemcomponents of method steps as “optimizers” or “optimization trainers,”and use of such terminology herein, is not to be interpreted asrequiring such components or steps to generate optimal results to theextreme (e.g., 100% prediction or classification accuracy). Rather, userof such terms is to be interpreted as indicating an attempt generate anoutput that is improved in some fashion relative to an input. Forexample, an optimization trainer that receives a trainable model asinput and outputs a trained model may attempt to improve a prediction orclassification accuracy of the trainable model by modifying one or moreattributes of the trainable model to generate the trained model. Geneticalgorithms are iterative adaptive search heuristics inspired bybiological natural selection. The genetic algorithm may start with apopulation of random models that each define a neural network withdifferent topology, weights and activation functions. Over the course ofseveral epochs (also known as generations), the models may be evolvedusing biology-inspired reproduction operations, such as crossover (e.g.,combining characteristics of two neural networks), mutation (e.g.,randomly modifying a characteristic of a neural network),stagnation/extinction (e.g., removing neural networks whose accuracy hasnot improved in several epochs), and selection (e.g., identifying thebest performing neural networks via testing). In addition, the bestperforming models of an epoch may be selected for reproduction togenerate a trainable model. The trainable model may be trained usingbackpropagation to generate a trained model. When the trained model isavailable, the trained model may be re-inserted into the geneticalgorithm for continued evolution. Training a model that is generated bybreeding the best performing population members of an epoch may serve toreinforce desired “genetic traits” (e.g., neural network topology,activation functions, connection weights, etc.), and introducing thetrained model back into the genetic algorithm may lead the geneticalgorithm to converge to an acceptably accurate solution (e.g., neuralnetwork) faster, for example because desired “genetic traits” areavailable for inheritance in later epochs of the genetic algorithm.

A computer system in accordance with the present disclosure may includea memory that stores an input data set and a plurality of datastructures. For example, each data structure may be a model of a neuralnetwork that models the input data set. The computer system may alsoinclude at least one processor that is configured to execute a recursivesearch. For example, the recursive search may be a genetic algorithm togenerate a neural network that best models the input data set. During afirst iteration of the recursive search, the processor may determine afitness value for each of the data structures (e.g., neural networkmodels) based on at least a subset of the input data set. The processormay also select a subset of data structures based on their respectivefitness values and may perform at least one of a crossover operation ora mutation operation with respect to at least one data structure of thesubset to generate a trainable data structure. The processor may furtherprovide the trainable data structure to an optimization trainer that isconfigured to train the trainable data structure based on a portion ofthe input data set to generate a trained structure and to provide thetrained data structure as input to a second iteration of the recursivesearch that is subsequent to the first iteration.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a particular example of a system that is operable tosupport cooperative execution of a genetic algorithm and abackpropagation trainer;

FIG. 2 illustrates a particular example of a model including datarepresentative of a neural network;

FIG. 3 illustrates particular examples of first and second stages ofoperation at the system of FIG. 1;

FIG. 4 illustrates particular examples of third and fourth stages ofoperation at the system of FIG. 1;

FIG. 5 illustrates a particular example of a fifth stage of operation atthe system of FIG. 1;

FIG. 6 illustrates a particular example of a sixth stage of operation atthe system of FIG. 1;

FIG. 7 illustrates a particular example of a seventh stage of operationat the system of FIG. 1; and

FIGS. 8A and 8B collectively illustrate a particular example of a methodof cooperative execution of a genetic algorithm and a backpropagationtrainer.

DETAILED DESCRIPTION

Referring to FIG. 1, a particular illustrative example of a system 100is shown. The system 100, or portions thereof, may be implemented using(e.g., executed by) one or more computing devices, such as laptopcomputers, desktop computers, mobile devices, servers, and Internet ofThings devices and other devices utilizing embedded processors andfirmware or operating systems, etc. In the illustrated example, thesystem 100 includes a genetic algorithm 110 and a backpropagationtrainer 180. The backpropagation trainer 180 is an example of anoptimization trainer, and other examples of optimization trainers thatmay be used in conjunction with the described techniques include, butare not limited to, a derivative free optimizer (DFO), an extremelearning machine (ELM), etc. In particular aspects, the geneticalgorithm 110 is executed on a different device, processor (e.g.,central processor unit (CPU), graphics processing unit (GPU) or othertype of processor), processor core, and/or thread (e.g., hardware orsoftware thread) than the backpropagation trainer 180. The geneticalgorithm 110 and the backpropagation trainer 180 may cooperate toautomatically generate a neural network model of a particular data set,such as an illustrative input data set 102. The system 100 may providean automated model building process that enables even inexperiencedusers to quickly and easily build highly accurate models based on aspecified data set. Additionally, the system 100 simplify the neuralnetwork model to avoid overfitting and to reduce computing resourcesrequired to run the model.

The genetic algorithm 110 includes or is otherwise associated with afitness function 140, a stagnation criterion 150, a crossover operation160, and a mutation operation 170. As described above, the geneticalgorithm 110 may represent a recursive search process. Consequently,each iteration of the search process (also called an epoch or generationof the genetic algorithm) may have an input set (or population) 120 andan output set (or population) 130. The input set 120 of an initial epochof the genetic algorithm 110 may be randomly or pseudo-randomlygenerated. After that, the output set 130 of one epoch may be the inputset 120 of the next (non-initial) epoch, as further described herein.

The input set 120 and the output set 130 may each include a plurality ofmodels, where each model includes data representative of a neuralnetwork. For example, each model may specify a neural network by atleast a neural network topology, a series of activation functions, andconnection weights. The topology of a neural network may include aconfiguration of nodes of the neural network and connections betweensuch nodes. The models may also be specified to include otherparameters, including but not limited to bias values/functions andaggregation functions.

Additional examples of neural network models are further described withreference to FIG. 2. In particular, as shown in FIG. 2, a model 200 maybe a data structure that includes node data 210 and connection data 220.In the illustrated example, the node data 210 for each node of a neuralnetwork may include at least one of an activation function, anaggregation function, or a bias (e.g., a constant bias value or a biasfunction). The activation function of a node may be a step function,sine function, continuous or piecewise linear function, sigmoidfunction, hyperbolic tangent function, or other type of mathematicalfunction that represents a threshold at which the node is activated. Thebiological analog to activation of a node is the firing of a neuron. Theaggregation function may be a mathematical function that combines (e.g.,sum, product, etc.) input signals to the node. An output of theaggregation function may be used as input to the activation function.The bias may be a constant value or function that is used by theaggregation function and/or the activation function to make the nodemore or less likely to be activated.

The connection data 220 for each connection in a neural network mayinclude at least one of a node pair or a connection weight. For example,if a neural network includes a connection from node N1 to node N2, thenthe connection data 220 for that connection may include the node pair<N1, N2>. The connection weight may be a numerical quantity thatinfluences if and/or how the output of N1 is modified before being inputat N2. In the example of a recurrent network, a node may have aconnection to itself (e.g., the connection data 220 may include the nodepair <N1, N1>).

The model 200 may also include a species identifier (ID) 230 and fitnessdata 240. The species ID 230 may indicate which of a plurality ofspecies the model 200 is classified in, as further described withreference to FIG. 3. The fitness data 240 may indicate how well themodel 200 models the input data set 102. For example, the fitness data240 may include a fitness value that is determined based on evaluatingthe fitness function 140 with respect to the model 200, as furtherdescribed herein.

Returning to FIG. 1, the fitness function 140 may be an objectivefunction that can be used to compare the models of the input set 120. Insome examples, the fitness function 140 is based on a frequency and/ormagnitude of errors produced by testing a model on the input data set102. As a simple example, assume the input data set 102 includes tenrows, that the input data set 102 includes two columns denoted A and B,and that the models illustrated in FIG. 1 represent neural networks thatoutput a predicted a value of B given an input value of A. In thisexample, testing a model may include inputting each of the ten values ofA from the input data set 102, comparing the predicted values of B tothe corresponding actual values of B from the input data set 102, anddetermining if and/or by how much the two predicted and actual values ofB differ. To illustrate, if a particular neural network correctlypredicted the value of B for nine of the ten rows, then the a relativelysimple fitness function 140 may assign the corresponding model a fitnessvalue of 9/10=0.9. It is to be understood that the previous example isfor illustration only and is not to be considered limiting. In someaspects, the fitness function 140 may be based on factors unrelated toerror frequency or error rate, such as number of input nodes, nodelayers, hidden layers, connections, computational complexity, etc.

In a particular aspect, fitness evaluation of models may be performed inparallel. To illustrate, the system 100 may include additional devices,processors, cores, and/or threads 190 to those that execute the geneticalgorithm 110 and the backpropagation trainer 180. These additionaldevices, processors, cores, and/or threads 190 may test model fitness inparallel based on the input data set 102 and may provide the resultingfitness values to the genetic algorithm 110.

In a particular aspect, the genetic algorithm 110 may be configured toperform speciation. For example, the genetic algorithm 110 may beconfigured to cluster the models of the input set 120 into species basedon “genetic distance” between the models. Because each model representsa neural network, the genetic distance between two models may be basedon differences in nodes, activation functions, aggregation functions,connections, connection weights, etc. of the two models. In anillustrative example, the genetic algorithm 110 may be configured toserialize a model into a bit string. In this example, the geneticdistance between models may be represented by the number of differingbits in the bit strings corresponding to the models. The bit stringscorresponding to models may be referred to as “encodings” of the models.Speciation is further described with reference to FIG. 3.

Because the genetic algorithm 110 is configured to mimic biologicalevolution and principles of natural selection, it may be possible for aspecies of models to become “extinct.” The stagnation criterion 150 maybe used to determine when a species should become extinct, e.g., whenthe models in the species are to be removed from the genetic algorithm110. Stagnation is further described with reference to FIG. 4.

The crossover operation 160 and the mutation operation 170 is highlystochastic under certain constraints and a defined set of probabilitiesoptimized for model building, which produces reproduction operationsthat can be used to generate the output set 130, or at least a portionthereof, from the input set 120. In a particular aspect, the geneticalgorithm 110 utilizes intra-species reproduction but not inter-speciesreproduction in generating the output set 130. Including intra-speciesreproduction and excluding inter-species reproduction may be based onthe assumption that because they share more genetic traits, the modelsof a species are more likely to cooperate and will therefore morequickly converge on a sufficiently accurate neural network. In someexamples, inter-species reproduction may be used in addition to orinstead of intra-species reproduction to generate the output set 130.Crossover and mutation are further described with reference to FIG. 6.

Left alone and given time to execute enough epochs, the geneticalgorithm 110 may be capable of generating a model (and by extension, aneural network) that meets desired accuracy requirements. However,because genetic algorithms utilize randomized selection, it may beoverly time-consuming for a genetic algorithm to arrive at an acceptableneural network. In accordance with the present disclosure, to “help” thegenetic algorithm 110 arrive at a solution faster, a model mayoccasionally be sent from the genetic algorithm 110 to thebackpropagation trainer 180 for training. This model is referred toherein as a trainable model 122. In particular, the trainable model 122may be based on crossing over and/or mutating the fittest models of theinput set 120, as further described with reference to FIG. 5. Thus, thetrainable model 122 may not merely be a genetically “trained” fileproduced by the genetic algorithm 110. Rather, the trainable model 122may represent an advancement with respect to the fittest models of theinput set 120.

The backpropagation trainer 180 may utilize a portion, but not all ofthe input data set 102 to train the connection weights of the trainablemodel 122, thereby generating a trained model 182. For example, theportion of the input data set 102 may be input into the trainable model122, which may in turn generate output data. The input data set 102 andthe output data may be used to determine an error value, and the errorvalue may be used to modify connection weights of the model, such as byusing gradient descent or another function.

The backpropagation trainer 180 may train using a portion rather thanall of the input data set 102 to mitigate overfit concerns and/or toshorten training time. The backpropagation trainer 180 may leave aspectsof the trainable model 122 other than connection weights (e.g., neuralnetwork topology, activation functions, etc.) unchanged. Backpropagatinga portion of the input data set 102 through the trainable model 122 mayserve to positively reinforce “genetic traits” of the fittest models inthe input set 120 that were used to generate the trainable model 122.Because the backpropagation trainer 180 may be executed on a differentdevice, processor, core, and/or thread than the genetic algorithm 110,the genetic algorithm 110 may continue executing additional epoch(s)while the connection weights of the trainable model 122 are beingtrained. When training is complete, the trained model 182 may be inputback into (a subsequent epoch of) the genetic algorithm 110, so that thepositively reinforced “genetic traits” of the trained model 182 areavailable to be inherited by other models in the genetic algorithm 110.

Operation of the system 100 is now described with reference to FIGS.3-7. It is to be understood, however, that in alternativeimplementations certain operations may be performed in a different orderthan described. Moreover, operations described as sequential may beinstead be performed at least partially concurrently, and operationsdescribed as being performed at least partially concurrently may insteadbe performed sequentially.

During a configuration stage of operation, a user may specify the inputdata set 102 and may specify a particular data field or a set of datafields in the input data set 102 to be modeled. The data field(s) to bemodeled may correspond to output nodes of a neural network that is to begenerated by the system 100. For example, if a user indicates that thevalue of a particular data field is to be modeled (e.g., to predict thevalue based on other data of the data set), the model may be generatedby the system 100 to include an output node that generates an outputvalue corresponding to a modeled value of the particular data field. Inparticular implementations, the user can also configure other aspects ofthe model. For example, the user may provide input to indicate aparticular data field of the data set that is to be included in themodel or a particular data field of the data set that is to be omittedfrom the model. As another example, the user may provide input toconstrain allowed model topologies. To illustrate, the model may beconstrained to include no more than a specified number of input nodes,no more than a specified number of hidden layers, or no recurrent loops.

Further, in particular implementations, the user can configure aspectsof the genetic algorithm 110. For example, the user may provide input tolimit a number of epochs that will be executed by the genetic algorithm110. Alternatively, the user may specify a time limit indicating anamount of time that the genetic algorithm 110 has to generate the model,and the genetic algorithm 110 may determine a number of epochs that willbe executed based on the specified time limit. To illustrate, an initialepoch of the genetic algorithm 110 may be timed (e.g., using a hardwareor software timer at the computing device executing the geneticalgorithm 110), and a total number of epochs that are to be executedwithin the specified time limit may be determined accordingly. Asanother example, the user may constrain a number of models evaluated ineach epoch, for example by constraining the size of the input set 120and/or the output set 130. As yet another example, the user can define anumber of trainable models 122 to be trained by the backpropagationtrainer 180 and fed back into the genetic algorithm 110 as trainedmodels 182.

In particular aspects, configuration of the genetic algorithm 110 alsoincludes performing pre-processing steps based on the input data set102. For example, the system 100 may determine, based on the input dataset 102 and/or user input, whether a neural network is to be generatedfor a regression problem, a classification problem, a reinforcementlearning problem, etc. As another example, the input data set 102 may be“cleaned” to remove obvious errors, fill in data “blanks,” etc. Asanother example, values in the input data set 102 may be scaled (e.g.,to values between 0 and 1). As yet another example, non-numerical data(e.g., categorical classification data or Boolean data) may be convertedinto numerical data.

After the above-described configuration stage, the genetic algorithm 110may automatically generate an initial set of models based on the inputdata set 102, received user input indicating (or usable to determine)the type of problem to be solved, etc. (e.g., the initial set of modelsis data-driven). As illustrated in FIG. 2, each model may be specifiedby at least a neural network topology, an activation function, and linkweights. The neural network topology may indicate an arrangement ofnodes (e.g., neurons). For example, the neural network topology mayindicate a number of input nodes, a number of hidden layers, a number ofnodes per hidden layer, and a number of output nodes. The neural networktopology may also indicate the interconnections (e.g., axons or links)between nodes.

The initial set of models may be input into an initial epoch of thegenetic algorithm 110 as the input set 120, and at the end of theinitial epoch, the output set 130 generated during the initial epoch maybecome the input set 120 of the next epoch of the genetic algorithm 110.In some examples, the input set 120 may have a specific number ofmodels. For example, as shown in a first stage 300 of operation in FIG.3, the input set may include 200 models. It is to be understood thatalternative examples may include a different number of models in theinput set 120 and/or the output set 130.

For the initial epoch of the genetic algorithm 110, the topologies ofthe models in the input set 120 may be randomly or pseudo-randomlygenerated within constraints specified by any previously inputconfiguration settings. Accordingly, the input set 120 may includemodels with multiple distinct topologies. For example, a first model mayhave a first topology, including a first number of input nodesassociated with a first set of data parameters, a first number of hiddenlayers including a first number and arrangement of hidden nodes, one ormore output nodes, and a first set of interconnections between thenodes. In this example, a second model of epoch may have a secondtopology, including a second number of input nodes associated with asecond set of data parameters, a second number of hidden layersincluding a second number and arrangement of hidden nodes, one or moreoutput nodes, and a second set of interconnections between the nodes.Since the first model and the second model are both attempting to modelthe same data field(s), the first and second models have the same outputnodes.

The genetic algorithm 110 may automatically assign an activationfunction, an aggregation function, a bias, connection weights, etc. toeach model of the input set 120 for the initial epoch. In some aspects,the connection weights are assigned randomly or pseudo-randomly. In someimplementations, a single activation function is used for each node of aparticular model. For example, a sigmoid function may be used as theactivation function of each node of the particular model. The singleactivation function may be selected based on configuration data. Forexample, the configuration data may indicate that a hyperbolic tangentactivation function is to be used or that a sigmoid activation functionis to be used. Alternatively, the activation function may be randomly orpseudo-randomly selected from a set of allowed activation functions, anddifferent nodes of a model may have different types of activationfunctions. In other implementations, the activation function assigned toeach node may be randomly or pseudo-randomly selected (from the set ofallowed activation functions) for each node the particular model.Aggregation functions may similarly be randomly or pseudo-randomlyassigned for the models in the input set 120 of the initial epoch. Thus,the models of the input set 120 of the initial epoch may have differenttopologies (which may include different input nodes corresponding todifferent input data fields if the data set includes many data fields)and different connection weights. Further, the models of the input set120 of the initial epoch may include nodes having different activationfunctions, aggregation functions, and/or bias values/functions.

Continuing to a second stage 350 of operation, each model of the inputset 120 may be tested based on the input data set 102 to determine modelfitness. For example, the input data set 102 may be provided as inputdata to each model, which processes the input data set (according to thenetwork topology, connection weights, activation function, etc., of therespective model) to generate output data. The output data of each modelmay be evaluated using the fitness function 140 to determine how wellthe model modeled the input data set 102. For example, in the case of aregression problem, the output data may be evaluated by comparing aprediction value in the output data to an actual value in the input dataset 102. As another example, in the case of a classification problem, aclassifier result indicated by the output data may be compared to aclassification associated with the input data set 102 to determine ifthe classifier result matches the classification in the input data set102. As yet another example, in the case of a reinforcement learningproblem, a reward may be determined (e.g., calculated) based onevaluation of an environment, which may include one or more variables,functions, etc. In a reinforcement learning problem, the fitnessfunction 140 may be the same as or may be based on the rewardfunction(s). Fitness of a model may be evaluated based on performance(e.g., accuracy) of the model, complexity (or sparsity) of the model, ora combination thereof. As a simple example, in the case of a regressionproblem or reinforcement learning problem, a fitness value may beassigned to a particular model based on an error value associated withthe output data of that model or based on the value of the rewardfunction, respectively. As another example, in the case of aclassification problem, the fitness value may be assigned based onwhether a classification determined by a particular model is a correctclassification, or how many correct or incorrect classifications weredetermined by the model.

In a more complex example, the fitness value may be assigned to aparticular model based on both prediction/classification accuracy orreward optimization as well as complexity (or sparsity) of the model. Asan illustrative example, a first model may model the data set well(e.g., may generate output data or an output classification with arelatively small error, or may generate a large positive reward functionvalue) using five input nodes (corresponding to five input data fields),whereas a second potential model may also model the data set well usingtwo input nodes (corresponding to two input data fields). In thisillustrative example, the second model may be sparser (depending on theconfiguration of hidden nodes of each network model) and therefore maybe assigned a higher fitness value that the first model.

As shown in FIG. 3, the second stage 350 may include clustering themodels into species based on genetic distance. In a particular aspect,the species ID 230 of each of the models may be set to a valuecorresponding to the species that the model has been clustered into.

Continuing to FIG. 4, during a third stage 400 and a fourth stage 450 ofoperation, a species fitness may be determined for each of the species.The species fitness of a species may be a function of the fitness of oneor more of the individual models in the species. As a simpleillustrative example, the species fitness of a species may be theaverage of the fitness of the individual models in the species. Asanother example, the species fitness of a species may be equal to thefitness of the fittest or least fit individual model in the species. Inalternative examples, other mathematical functions may be used todetermine species fitness. The genetic algorithm 110 may maintain a datastructure that tracks the fitness of each species across multipleepochs. Based on the species fitness, the genetic algorithm 110 mayidentify the “fittest” species, shaded and denoted in FIG. 4 as “elitespecies.” Although three elite species 410, 420, and 430 are shown inFIG. 4, it is to be understood that in alternate examples a differentnumber of elite species may be identified.

In a particular aspect, the genetic algorithm 110 uses species fitnessto determine if a species has become stagnant and is therefore to becomeextinct. As an illustrative non-limiting example, the stagnationcriterion 150 may indicate that a species has become stagnant if thefitness of that species remains within a particular range (e.g., +/−5%)for a particular number (e.g., 5) epochs. If a species satisfies astagnation criteria, the species and all underlying models may beremoved from the genetic algorithm 110. In the illustrated example,species 360 of FIG. 3 is removed, as shown in the third stage 400through the use of broken lines.

Proceeding to the fourth stage 450, the fittest models of each “elitespecies” may be identified. The fittest models overall may also beidentified. In the illustrated example, the three fittest models of each“elite species” are denoted “elite members” and shown using a hatchpattern. Thus, model 470 is an “elite member” of the “elite species”420. The three fittest models overall are denoted “overall elites” andare shown using black circles. Thus, models 460, 462, and 464 are the“overall elites” in the illustrated example. As shown in FIG. 4 withrespect to the model 460, an “overall elite” need not be an “elitemember,” e.g., may come from a non-elite species. In an alternateimplementation, a different number of “elite members” per species and/ora different number of “overall elites” may be identified.

Referring now to FIG. 5, during a fifth stage 500 of operation, the“overall elite” models 460, 462, and 464 may be genetically combined togenerate the trainable model 122. For example, genetically combiningmodels may include crossover operations in which a portion of one modelis added to a portion of another model, as further illustrated in FIG.6. As another example, a random mutation may be performed on a portionof one or more of the “overall elite” models 460, 462, 464 and/or thetrainable model 122. The trainable model 122 may be sent to thebackpropagation trainer 180, as described with reference to FIG. 1. Thebackpropagation trainer 180 may train connection weights of thetrainable model 122 based on a portion of the input data set 102. Whentraining is complete, the resulting trained model 182 may be receivedfrom the backpropagation trainer 180 and may be input into a subsequentepoch of the genetic algorithm 110.

Continuing to FIG. 6, while the backpropagation trainer 180 trains thetrainable model, the output set 130 of the epoch may be generated in asixth stage 600 of operation. In the illustrated example, the output set130 includes the same number of models, e.g., 200 models, as the inputset 120. The output set 130 may include each of the “overall elite”models 460-464. The output set 130 may also include each of the “elitemember” models, including the model 470. Propagating the “overall elite”and “elite member” models to the next epoch may preserve the “genetictraits” resulted in caused such models being assigned high fitnessvalues.

The rest of the output set 130 may be filled out by random intra-speciesreproduction using the crossover operation 160 and/or the mutationoperation 170. In the illustrated example, the output set 130 includes10 “overall elite” and “elite member” models, so the remaining 190models may be randomly generated based on intra-species reproductionusing the crossover operation 160 and/or the mutation operation 170.After the output set 130 is generated, the output set 130 may beprovided as the input set 120 for the next epoch of the geneticalgorithm 110.

During the crossover operation 160, a portion of one model may becombined with a portion of another model, where the size of therespective portions may or may not be equal. To illustrate withreference to the model “encodings” described with respect to FIG. 1, thecrossover operation 160 may include concatenating bits 0 to p of one bitstring with bits p+1 to q of another bit string, where p and q areintegers and p+q is equal to the total size of a bit string thatrepresents a model resulting from the crossover operation 160. Whendecoded, the resulting bit string after the crossover operation 160produces a neural network that differs from each of its “parent” neuralnetworks in terms of topology, activation function, aggregationfunction, bias value/function, link weight, or any combination thereof.

Thus, the crossover operation 160 may be a random or pseudo-randombiological operator that generates a model of the output set 130 bycombining aspects of a first model of the input set 120 with aspects ofone or more other models of the input set 120. For example, thecrossover operation 160 may retain a topology of hidden nodes of a firstmodel of the input set 120 but connect input nodes of a second model ofthe input set to the hidden nodes. As another example, the crossoveroperation 160 may retain the topology of the first model of the inputset 120 but use one or more activation functions of the second model ofthe input set 120. In some aspects, rather than operating on models ofthe input set 120, the crossover operation 160 may be performed on amodel (or models) generated by mutation of one or more models of theinput set 120. For example, the mutation operation 170 may be performedon a first model of the input set 120 to generate an intermediate modeland the crossover operation 160 may be performed to combine aspects ofthe intermediate model with aspects of a second model of the input set120 to generate a model of the output set 130.

During the mutation operation 170, a portion of a model may be randomlymodified. The frequency of mutations may be based on a mutationprobability metric, which may be user-defined or randomlyselected/adjusted. To illustrate with reference to the model “encodings”described with respect to FIG. 1, the mutation operation 170 may includerandomly “flipping” one or more bits a bit string.

The mutation operation 170 may thus be a random or pseudo-randombiological operator that generates or contributes to a model of theoutput set 130 by mutating any aspect of a model of the input set 120.For example, the mutation operation 170 may cause the topology aparticular model of the input set to be modified by addition or omissionof one or more input nodes, by addition or omission of one or moreconnections, by addition or omission of one or more hidden nodes, or acombination thereof. As another example, the mutation operation 170 maycause one or more activation functions, aggregation functions, biasvalues/functions, and/or or connection weights to be modified. In someaspects, rather than operating on a model of the input set, the mutationoperation 170 may be performed on a model generated by the crossoveroperation 160. For example, the crossover operation 160 may combineaspects of two models of the input set 120 to generate an intermediatemodel and the mutation operation 170 may be performed on theintermediate model to generate a model of the output set 130.

The genetic algorithm 110 may continue in the manner described abovethrough multiple epochs. When the genetic algorithm 110 receives thetrained model 182, the trained model 182 may be provided as part of theinput set 120 of the next epoch, as shown in a seventh stage 700 of FIG.7. For example, the trained model 182 may replace one of the other 200models in the input set 120 or may be a 201^(st) model of the input set(e.g., in some epochs, more than 200 models may be processed). Duringtraining by the backpropagation trainer 180, the genetic algorithm 110may have advanced one or more epochs. Thus, when the trained model 182is received, the trained model 182 may be inserted as input into anepoch subsequent to the epoch during which the corresponding trainablemodel 122 was provided to the backpropagation trainer 180. Toillustrate, if the trainable model 122 was provided to thebackpropagation trainer 180 during epoch N, then the trained model 182may be input into epoch N+X, where X is an integer greater than zero.

In the example of FIGS. 5 and 7, a single trainable model 122 isprovided to the backpropagation trainer 180 and a single trained model182 is received from the backpropagation trainer 180. When the trainedmodel 182 is received, the backpropagation trainer 180 becomes availableto train another trainable model. Thus, because training takes more thanone epoch, trained models 182 may be input into the genetic algorithm110 sporadically rather than every epoch after the initial epoch. Insome implementations, the backpropagation trainer 180 may have a queueor stack of trainable models 122 that are awaiting training. The geneticalgorithm 110 may add trainable models 122 to the queue or stack as theyare generated and the backpropagation trainer 180 may remove a trainingmodel 122 from the queue or stack at the start of a training cycle. Insome implementations, the system 100 includes multiple backpropagationtrainers 180 (e.g., executing on different devices, processors, cores,or threads). Each of the backpropagation trainers 180 may be configuredto simultaneously train a different trainable model 122 to generate adifferent trained model 182. In such examples, more than one trainablemodel 122 may be generated during an epoch and/or more than one trainedmodel 182 may be input into an epoch.

Operation at the system 100 may continue iteratively until specified atermination criterion, such as a time limit, a number of epochs, or athreshold fitness value (of an overall fittest model) is satisfied. Whenthe termination criterion is satisfied, an overall fittest model of thelast executed epoch may be selected and output as representing a neuralnetwork that best models the input data set 102. In some examples, theoverall fittest model may undergo a final training operation (e.g., bythe backpropagation trainer 180) before being output.

Although various aspects are described with reference to abackpropagation training, it is to be understood that in alternateimplementations different types of training may also be used in thesystem 100. For example, models may be trained using a genetic algorithmtraining process. In this example, genetic operations similar to thosedescribed above are performed while all aspects of a model, except forthe connection weight, are held constant.

Performing genetic operations may be less resource intensive thanevaluating fitness of models and training of models usingbackpropagation. For example, both evaluating the fitness of a model andtraining a model include providing the input data set 102, or at least aportion thereof, to the model, calculating results of nodes andconnections of a neural network to generate output data, and comparingthe output data to the input data set 102 to determine the presenceand/or magnitude of an error. In contrast, genetic operations do notoperate on the input data set 102, but rather merely modifycharacteristics of one or more models. However, as described above, oneiteration of the genetic algorithm 110 may include both geneticoperations and evaluating the fitness of every model and species.Training trainable models generated by breeding the fittest models of anepoch may improve fitness of the trained models without requiringtraining of every model of an epoch. Further, the fitness of models ofsubsequent epochs may benefit from the improved fitness of the trainedmodels due to genetic operations based on the trained models.Accordingly, training the fittest models enables generating a model witha particular error rate in fewer epochs than using genetic operationsalone. As a result, fewer processing resources may be utilized inbuilding highly accurate models based on a specified input data set 102.

The system 100 of FIG. 1 may thus support cooperative, data-drivenexecution of a genetic algorithm and a backpropagation trainer toautomatically arrive at an output neural network model of an input dataset. The system of FIG. 1 may arrive at the output neural network modelfaster than using a genetic algorithm or backpropagation alone and withreduced cost as compared to hiring a data scientist. In some cases, theneural network model output by the system 100 may also be more accuratethan a model that would be generated by a genetic algorithm orbackpropagation alone. The system 100 may also provide aproblem-agnostic ability to generate neural networks. For example, thesystem 100 may represent a single automated model building frameworkthat is capable of generating neural networks for at least regressionproblems, classification problems, and reinforcement learning problems.Further, the system 100 may enable generation of a generalized neuralnetwork that demonstrates improved adaptability to never-before-seenconditions. To illustrate, the neural network may mitigate or avoidoverfitting to an input data set and instead may be more universal innature. Thus, the neural networks generated by the system 100 may becapable of being deployed with fewer concerns about generating incorrectpredictions.

It will be appreciated that the systems and methods of the presentdisclosure may be applicable in various scenarios, infrastructures, anddata environments. As an illustrative non-limiting example, the inputdata set 102 may include timestamped data from a large array of sensorsdistributed around a wind farm and may also include timestampeduptime/downtime data of individual wind turbines. The system 100 maygenerate a neural network model that is configured how likely a windturbine is to fail. The neural network model may, in a particularexample, increase failure lead time from 3-5 days to 30-40 days, whichcan result in reduced downtime and monetary savings for an operator ofthe wind farm. The system 100 may be capable of automatically buildingsimilar kinds of models that predict numerical values or states (e.g.,failures) for internet of things (IoT), utilities, and oil/gasinfrastructures.

As another illustrative non-limiting example, the input data set 102 mayinclude health data and the system 100 may automatically build a modelto predict whether a patient exhibiting certain health conditions islikely to have a particular ailment. As another illustrativenon-limiting example, the input data set 102 may include financial dataand the system 100 may automatically build a model to forecast marketconditions. As another illustrative non-limiting example, the input dataset 102 may include network security, network log, and/or malware data,and the system 100 may automatically build a model to implement firewallfiltering rules, endpoint anti-malware detection, a bot/botnet detector,etc.

As another illustrative non-limiting example, the system 100 maygenerate a neural network to output aircraft auto-pilot operations (e.g.throttle, steer, flaps, etc.) based on reinforcement learning. In suchan example, the reward function optimized by the neural network mayinvolve aircraft altitude, aircraft distance traveled, etc. As yetanother example, the system 100 may generate a neural network to predictoil/gas industry workover events (e.g., events that lead to majormaintenance or remedial operations on a rig or well, which can lead toconsiderable production time lost and expense incurred).

Yet another example of a problem set that can be solved with neuralnetworks generated with the system described herein is data fusion. Inthis case, data aggregated from a large number of sensors of varioustypes, including multiple sensors of the same type, is collected andused to identify an object, action or phenomenon that wouldn't beentirely detectable with any one, or a small subset of sensors. Forexample, the detection of a submarine may be performed based on theinputs received from multiple sonar buoys which provide input to thegenerated neural network. Another example may be the identification of aparticular type of aircraft based on both the audio signature and avisual view (which may be partially obscured, or low resolution).

FIGS. 8A and 8B depict a particular example of a method 800 ofcooperative execution of a genetic algorithm and a backpropagationtrainer. In an illustrative example, the method 800 may be performed atthe system 100 of FIG. 1.

The method 800 may start, at 802, and may include generating arandomized input population of models based on an input data set, at804. Each model may include data representative of a neural network. Forexample, each model may include at least node data and connection data,as described with reference to FIGS. 1 and 2. Further, each of themodels may be part of the input set 120 of FIG. 1 and may model theinput data set 102 of FIG. 1.

The method 800 may also include determining, based on a fitnessfunction, a fitness value of each model of the input population, at 806.For example, the fitness of each model of the input set 120 may bedetermined, as described with reference to FIGS. 1 and 3.

The method 800 may further include determining a subset of models basedon their respective fitness values, at 808. The subset of models may bethe fittest models of the input population, e.g., “overall elites.” Forexample, “overall elites” may be determined as described with referenceto FIGS. 1 and 4.

The method 800 may include performing multiple sets of operations atleast partially concurrently. Continuing to 826 (in FIG. 8B), the method800 may include performing at least one genetic operation with respectto at least one model of the subset to generate a trainable model. Forexample, the crossover operation 160 and/or the mutation operation 170may be performed with respect to the “overall elites” to generate thetrainable model 122, as described with reference to FIGS. 1, 4, and 5.

The method 800 may also include sending the trainable model to abackpropagation trainer (or other optimization trainer) for trainingbased on a portion of the input data set, at 828. For example, thebackpropagation trainer 180 of FIG. 1 may train the trainable model 122based on a portion of the input data set 102 to generate the trainedmodel 182, as described with reference to FIGS. 1 and 5.

The genetic algorithm may continue while backpropagation trainingoccurs. For example, the method 800 may include grouping the inputpopulation of models into species based on genetic distance, at 810, anddetermining species fitness of each species, at 812. To illustrate, themodels of the input set 120 may be grouped into species and speciesfitness may be evaluated as described with reference to FIGS. 1, 3, and4.

Continuing to 814, species that satisfy a stagnation criteria may beremoved. For example, species satisfying the stagnation criterion 150may be removed, as described with reference to FIGS. 1 and 4. At 816,the method 800 may include identifying a subset of species based ontheir respective fitness values and identifying models of each speciesin the subset based on their respective model fitness values. The subsetof species may be the fittest species of the input population, e.g.,“elite species,” and the identified models of the “elite species” may bethe fittest members of those species, e.g., “elite members.” Forexample, species fitness values, “elite species,” and “elite members”may be determined as described with reference to FIGS. 1 and 4.

The method 800 may include determining an output population thatincludes each “elite member,” the “overall elites,” and at least onemodel that is generated based on intra-species reproduction, at 818. Forexample, the models of the output set 130 of FIG. 1 may be determined,where the output set 130 includes the overall elite models 460-464, theelite members (including the elite member model 470), and at least onemodel generated based on intra-species reproduction using the crossoveroperation 160 and/or the mutation operation 170, as described withreference to FIGS. 1 and 6.

The method 800 may include determining whether a termination criterionis satisfied, at 820. The termination criterion may include a timelimit, a number of epochs, or a threshold fitness value of an overallfittest model, as illustrative non-limiting examples. If the terminationcriterion is not satisfied, the method 800 returns to 806 and a nextepoch of the genetic algorithm is executed, where the output populationdetermined at 818 is the input population of the next epoch.

As described above, while the genetic algorithm is ongoing, thebackpropagation trainer may train the trainable model to generate atrained model. When training is complete, the method 800 may includereceiving the trained model from the backpropagation trainer (or otheroptimization trainer), at 830 (in FIG. 8B). The trained model may beadded to the input set of an epoch of the genetic algorithm, as shown inFIG. 8B.

When the termination criterion is satisfied, at 820, the method 800 mayinclude selecting and outputting a fittest model, at 822, and the method800 may end, at 824. In some implementations, the selected model may besubjected to a final training operation, e.g., by the backpropagationtrainer or by another trainer, before being output.

It is to be understood that the division and ordering of steps in FIGS.8A and 8B is for illustrative purposes only and is not be consideredlimiting. In alternative implementations, certain steps may be combinedand other steps may be subdivided into multiple steps. Moreover, theordering of steps may change. For example, the termination criterion maybe evaluated after determining the “overall elites,” at 808, rather thanafter determining the output population, at 818.

In conjunction with the described aspects, a computer system may includea memory configured to store an input data set and a plurality of datastructures, each of the plurality of data structures including datarepresentative of a neural network. The system also includes a processorconfigured to execute a recursive search. Executing the recursive searchincludes, during a first iteration: determining a fitness value for eachof the plurality of data structures based on at least a subset of theinput data set, selecting a subset of data structures from the pluralityof data structures based on the fitness values of the subset of datastructures, performing at least one of a crossover operation or amutation operation with respect to at least one data structure of thesubset to generate a trainable data structure, and providing thetrainable data structure to an optimization trainer. The optimizationtrainer is configured to train the trainable data structure based on aportion of the input data set to generate a trained data structure andto provide the trained data structure as input to a second iteration ofthe recursive search that is subsequent to the first iteration.

In conjunction with the described aspects, a method includes, based on afitness function, selecting, by a processor of a computing device, asubset of models from a plurality of models. The plurality of models isgenerated based on a genetic algorithm and corresponds to a first epochof the genetic algorithm. Each of the plurality of models includes datarepresentative of a neural network. The method also includes performingat least one genetic operation of the genetic algorithm with respect toat least one model of the subset to generate a trainable model andsending the trainable model to an optimization trainer. The methodincludes adding a trained model received from the optimization traineras input to a second epoch of the genetic algorithm that is subsequentto the first epoch.

In conjunction with the described aspects, a computer-readable storagedevice stores instructions that, when executed, cause a computer toperform operations including, based on a fitness function, selecting asubset of models from a plurality of models. The plurality of models isgenerated based on a genetic algorithm and corresponds to a first epochof the genetic algorithm. Each of the plurality of models includes datarepresentative of a neural network. The operations also includeperforming at least one genetic operation of the genetic algorithm withrespect to at least one model of the subset to generate a trainablemodel and sending the trainable model to a trainer. The operationsinclude adding a trained model received from the trainer as input to asecond epoch of the genetic algorithm that is subsequent to the firstepoch.

The systems and methods illustrated herein may be described in terms offunctional block components, screen shots, optional selections andvarious processing steps. It should be appreciated that such functionalblocks may be realized by any number of hardware and/or softwarecomponents configured to perform the specified functions. For example,the system may employ various integrated circuit components, e.g.,memory elements, processing elements, logic elements, look-up tables,and the like, which may carry out a variety of functions under thecontrol of one or more microprocessors or other control devices.Similarly, the software elements of the system may be implemented withany programming or scripting language such as C, C++, C#, Java,JavaScript, VBScript, Macromedia Cold Fusion, COBOL, Microsoft ActiveServer Pages, assembly, PERL, PHP, AWK, Python, Visual Basic, SQL StoredProcedures, PL/SQL, any UNIX shell script, and extensible markuplanguage (XML) with the various algorithms being implemented with anycombination of data structures, objects, processes, routines or otherprogramming elements. Further, it should be noted that the system mayemploy any number of techniques for data transmission, signaling, dataprocessing, network control, and the like.

The systems and methods of the present disclosure may be embodied as acustomization of an existing system, an add-on product, a processingapparatus executing upgraded software, a standalone system, adistributed system, a method, a data processing system, a device fordata processing, and/or a computer program product. Accordingly, anyportion of the system or a module may take the form of a processingapparatus executing code, an internet based (e.g., cloud computing)embodiment, an entirely hardware embodiment, or an embodiment combiningaspects of the internet, software and hardware. Furthermore, the systemmay take the form of a computer program product on a computer-readablestorage medium or device having computer-readable program code (e.g.,instructions) embodied or stored in the storage medium or device. Anysuitable computer-readable storage medium or device may be utilized,including hard disks, CD-ROM, optical storage devices, magnetic storagedevices, and/or other storage media. A computer-readable storage mediumor device is not a signal.

Systems and methods may be described herein with reference to screenshots, block diagrams and flowchart illustrations of methods,apparatuses (e.g., systems), and computer media according to variousaspects. It will be understood that each functional block of a blockdiagrams and flowchart illustration, and combinations of functionalblocks in block diagrams and flowchart illustrations, respectively, canbe implemented by computer program instructions.

Computer program instructions may be loaded onto a computer or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions that execute on the computer or other programmable dataprocessing apparatus create means for implementing the functionsspecified in the flowchart block or blocks. These computer programinstructions may also be stored in a computer-readable memory or devicethat can direct a computer or other programmable data processingapparatus to function in a particular manner, such that the instructionsstored in the computer-readable memory produce an article of manufactureincluding instruction means which implement the function specified inthe flowchart block or blocks. The computer program instructions mayalso be loaded onto a computer or other programmable data processingapparatus to cause a series of operational steps to be performed on thecomputer or other programmable apparatus to produce acomputer-implemented process such that the instructions which execute onthe computer or other programmable apparatus provide steps forimplementing the functions specified in the flowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions.

Although the disclosure may include a method, it is contemplated that itmay be embodied as computer program instructions on a tangiblecomputer-readable medium, such as a magnetic or optical memory or amagnetic or optical disk/disc. All structural, chemical, and functionalequivalents to the elements of the above-described exemplary embodimentsthat are known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassed bythe present claims. Moreover, it is not necessary for a device or methodto address each and every problem sought to be solved by the presentdisclosure, for it to be encompassed by the present claims. Furthermore,no element, component, or method step in the present disclosure isintended to be dedicated to the public regardless of whether theelement, component, or method step is explicitly recited in the claims.As used herein, the terms “comprises”, “comprising”, or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises a list ofelements does not include only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus.

Changes and modifications may be made to the disclosed embodimentswithout departing from the scope of the present disclosure. These andother changes or modifications are intended to be included within thescope of the present disclosure, as expressed in the following claims.

What is claimed is:
 1. A computer system comprising: a memory configuredto store an input data set and a plurality of data structures, each ofthe plurality of data structures including data representative of aneural network; a processor configured to execute a recursive search,wherein executing the recursive search comprises, during a firstiteration: determining a trainable data structure based on modificationof one or more data structures of the plurality of data structures; andproviding the trainable data structure to an optimization trainer, theoptimization trainer configured to: train the trainable data structurebased on a portion of the input data set to generate a trained datastructure; and provide the trained data structure as input to a seconditeration of the recursive search, the second iteration subsequent tothe first iteration.
 2. The computer system of claim 1, whereinexecuting the recursive search further comprises, during the firstiteration, selecting the one or more data structures based on theirrespective fitness values.
 3. The computer system of claim 1, whereinthe modification corresponds to at least one of a crossover operation ora mutation operation with respect to the one or more data structures. 4.The computer system of claim 1, wherein the optimization trainer isexecuted on a different device, graphics processing unit (GPU),processor, core, thread, or any combination thereof, than the recursivesearch.
 5. A method comprising: determining, by a processor of acomputing device, a trainable model to provide to an optimizationtrainer, the trainable model determined based on modification of one ormore models of a plurality of models, the plurality of models generatedbased on a genetic algorithm and corresponding to a first epoch of thegenetic algorithm, wherein each of the plurality of models includes datarepresentative of a neural network; providing the trainable model to anoptimization trainer; and adding a trained model, output by theoptimization trainer based on the trainable model, as input to a secondepoch of the genetic algorithm, the second epoch subsequent to the firstepoch.
 6. The method of claim 5, further comprising selecting the one ormore models based on their respective fitness values.
 7. The method ofclaim 5, wherein the trainable model is generated by performing at leastone of a crossover operation or a mutation operation with respect to theone or more models.
 8. The method of claim 5, wherein the optimizationtrainer is configured to use a portion of an input data set associatedwith the genetic algorithm to train the trainable model.
 9. The methodof claim 5, wherein the data representative of the neural networkincludes node data corresponding to a plurality of nodes of the neuralnetwork and connection data corresponding to one or more connections ofthe neural network, wherein the node data corresponding to a particularnode includes an activation function, an aggregation function, a bias,or any combination thereof, and wherein the connection datacorresponding to a particular connection includes node pairs, connectionweights, or any combination thereof.
 10. The method of claim 5, whereinthe optimization trainer is configured to update connection weights ofthe trainable model without updating a topology or activation functionsof the trainable model.
 11. The method of claim 5, wherein the firstepoch is an initial epoch of the genetic algorithm.
 12. The method ofclaim 5, wherein the first epoch is a non-initial epoch of the geneticalgorithm.
 13. The method of claim 5, wherein the second epoch and thefirst epoch are separated by at least one intervening epoch.
 14. Themethod of claim 5, wherein each of the plurality of models includes atleast one output node configured to generate an output valuecorresponding to a field of an input data set associated with thegenetic algorithm, and wherein a fitness value of a particular model isbased at least partially on a comparison of the output value and thefield of the input data set.
 15. The method of claim 5, wherein each ofthe plurality of models includes at least one output node configured togenerate a classifier result.
 16. The method of claim 5, wherein theplurality of models corresponds to an input population of the firstepoch, and further comprising: grouping the models of the plurality ofmodels into species based on genetic distance between the models;determining a species fitness value of each of the species; selectivelyremoving one or more species from the genetic algorithm responsive todetermining that the one or more species satisfy a stagnation criterion;determining one or more elite species based on their respective speciesfitness values; identifying elite members of each elite species; andgenerating an output population to be input into the second epoch,wherein the output population includes each of the elite members, and atleast one of a first model generated based on intra-species reproductionor a second model generated based on inter-species reproduction.
 17. Themethod of claim 16, wherein the output population further includes thetrained model output by the optimization trainer.
 18. A non-transitorycomputer-readable storage device storing instructions that, whenexecuted, cause a computer to perform operations comprising: determininga trainable model to provide to a trainer, the trainable modeldetermined based on modification of one or more models of a plurality ofmodels, the plurality of models generated based on a genetic algorithmand corresponding to a first epoch of the genetic algorithm, whereineach of the plurality of models includes data representative of a neuralnetwork; providing the trainable model to the trainer; and adding atrained model, output by the trainer based on the trainable model, asinput to a second epoch of the genetic algorithm, the second epochsubsequent to the first epoch.
 19. The non-transitory computer-readablestorage device of claim 18, wherein the trainer comprises abackpropagation trainer.
 20. The non-transitory computer-readablestorage device of claim 18, wherein: the operations further compriseselecting the one or more models based on their respective fitnessvalues; the trainable model is generated by performing at least one of acrossover operation or a mutation operation with respect to the one ormore models; or a combination thereof.